Cramer's V and its confidence interval

These functions compute the point estimate and confidence interval for Cramer's V.

cramersV(x, y = NULL, digits = 2)

# S3 method for CramersV
print(x, digits = x$input$digits, ...)

confIntV(
  x,
  y = NULL,
  conf.level = 0.95,
  samples = 500,
  digits = 2,
  method = c("bootstrap", "fisher"),
  storeBootstrappingData = FALSE
)

# S3 method for confIntV
print(x, digits = x$input$digits, ...)

Arguments

x: Either a crosstable to analyse, or one of two vectors to use to generate that crosstable. The vector should be a factor, i.e. a categorical variable identified as such by the 'factor' class).
y: If x is a crosstable, y can (and should) be empty. If x is a vector, y must also be a vector.
digits: Minimum number of digits after the decimal point to show in the result.
...: Any additional arguments are passed on to the print function.
conf.level: Level of confidence for the confidence interval.
samples: Number of samples to generate when bootstrapping.
method: Whether to use Fisher's Z or bootstrapping to compute the confidence interval.
storeBootstrappingData: Whether to store (or discard) the data generating during the bootstrapping procedure.

Value

A point estimate or a confidence interval for Cramer's V, an effect size to describe the association between two categorical variables.

Examples

### Get confidence interval for Cramer's V
### Note that by using 'table', and so removing the raw data, inhibits
### bootstrapping, which could otherwise take a while.
confIntV(table(infert$education, infert$induced));
#> Cramér's V 95% confidence interval (point estimate = .18):  
#> Using Fisher's z: [.06; .3]